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Prep plan
Updated weekly · Last refresh Aug 30

Zensar Technologies GenAI Engineer Interview Questions

The questions to prepare for a Zensar Technologies GenAI Engineer interview. Questions from real interview reports rank first. Updated weekly.

20questions
~3htotal time
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1
Machine LearningStart here. 4 questions · ~33 min
Handling Missing Values in MLEasy

Explain practical strategies for handling missing values in a supervised learning workflow, from diagnosis to modeling and validation.

Cross-ValidationFeature EngineeringRegularizationZensar Technologies
Experience with ML TechniquesEasy

Describe your hands-on experience applying supervised learning, feature engineering, and model evaluation in real projects.

Cross-ValidationFeature EngineeringSupervised LearningZensar Technologies
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2
Generative AI & LLMs4 questions · ~33 min
Evaluate an LLM SystemMedium

Explain how to evaluate a generative model using offline and online methods, with attention to hallucination, product metrics, and experiment design.

HallucinationPrompt EngineeringLLM EvaluationZensar Technologies
Architecting GenAI for ContentHard

Tests system design skills for deploying GenAI workflows end to end with reliability and quality controls.

Prompt EngineeringRAGLLM EvaluationZensar Technologies
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3
Coding3 questions · ~25 min
Implementing a Simple GANHard

Tests ability to code and reason about GAN training dynamics, loss functions, and model components.

Neural NetworksMathGradient DescentZensar Technologies
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4
System Design3 questions · ~25 min
Cloud vs On-Prem AI Trade-offsMedium

Tests ability to weigh cost, latency, security, compliance, and scalability for GenAI deployments.

InfrastructureModel ServingZensar Technologies
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5
Behavioral & Leadership4 questions · ~33 min
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6
More topics2 questions · ~16 min
Improve Model AccuracyMedium

Approach for improving a model's accuracy by checking errors, features, and tuning choices.

Hyperparameter TuningCross-ValidationAccuracyZensar Technologies
Preprocessing Text for NLPEasy

Tests practical NLP data preparation skills like cleaning, tokenization, and encoding.

LemmatizationStemmingTokenizationZensar Technologies
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